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Ma G, Kang J, Yu T. Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes. Brief Bioinform 2024; 25:bbae141. [PMID: 38581417 PMCID: PMC10998539 DOI: 10.1093/bib/bbae141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/28/2024] [Accepted: 03/13/2024] [Indexed: 04/08/2024] Open
Abstract
Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.
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Affiliation(s)
- Guoxuan Ma
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tianwei Yu
- Shenzhen Research Institute of Big Data, School of Data Science, The Chinese University of Hong Kong - Shenzhen (CUHK-Shenzhen), Shenzhen, Guangdong 518172, China
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Tian L, Yu T. An integrated deep learning framework for the interpretation of untargeted metabolomics data. Brief Bioinform 2023; 24:bbad244. [PMID: 37369636 DOI: 10.1093/bib/bbad244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 06/02/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.
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Affiliation(s)
- Leqi Tian
- School of Data Science, The Chinese University of Hong Kong - Shenzhen, Guangdong, China
- Shenzhen Research Institute of Big Data, Guangdong, China
| | - Tianwei Yu
- School of Data Science, The Chinese University of Hong Kong - Shenzhen, Guangdong, China
- Shenzhen Research Institute of Big Data, Guangdong, China
- Guangdong Provincial Key Laboratory of Big Data Computing, Guangdong, China
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Tian L, Li Z, Ma G, Zhang X, Tang Z, Wang S, Kang J, Liang D, Yu T. Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data. Bioinformatics 2022; 38:3662-3664. [PMID: 35639952 PMCID: PMC9272804 DOI: 10.1093/bioinformatics/btac364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/07/2022] [Accepted: 05/25/2022] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (1) matching uncertainty between data features and metabolites; (2) lacking of method to analyze positive mode and negative mode LC/MS data simultaneously on the same set of subjects; (3) the incompleteness of pathways in individual software packages. RESULTS We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration - (1) a weighted GSEA-type test, and (2) a permutation-based weighted hypergeometric test. The package is capable of combining positive and negative ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: KEGG, Mummichog, and SMPDB. AVAILABILITY The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leqi Tian
- Shenzhen Research Institute of Big Data.,School of Data Science, The Chinese University of Hong Kong-Shenzhen
| | - Zhenjiang Li
- Gangarosa Department of Environmental Health, Emory University
| | - Guoxuan Ma
- School of Data Science, The Chinese University of Hong Kong-Shenzhen.,Department of Biostatistics, University of Michigan
| | - Xiaoyue Zhang
- Gangarosa Department of Environmental Health, Emory University
| | - Ziyin Tang
- Gangarosa Department of Environmental Health, Emory University
| | - Siheng Wang
- School of Data Science, The Chinese University of Hong Kong-Shenzhen
| | - Jian Kang
- Department of Biostatistics, University of Michigan
| | - Donghai Liang
- Gangarosa Department of Environmental Health, Emory University
| | - Tianwei Yu
- Shenzhen Research Institute of Big Data.,School of Data Science, The Chinese University of Hong Kong-Shenzhen.,Warshel Institute, Shenzhen, Guangdong, China
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Tokuoka SM, Kita Y, Sato M, Shimizu T, Yatomi Y, Oda Y. Lipid Profiles of Human Serum Fractions Enhanced with CD9 Antibody-Immobilized Magnetic Beads. Metabolites 2022; 12:metabo12030230. [PMID: 35323673 PMCID: PMC8956076 DOI: 10.3390/metabo12030230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/03/2022] [Accepted: 03/04/2022] [Indexed: 11/16/2022] Open
Abstract
Blood samples are minimally invasive and can be collected repeatedly, but they are far from the site of disease and the target molecules are diluted by the large amount of blood. Therefore, we performed lipidomics using immunoprecipitation as a method to enrich specific fractions of serum. In this study, a CD9 antibody was immobilized on magnetic beads to enrich CD9-containing components in the serum for lipidomics. The percentages of phospholipids recovered from serum by methanol and isopropanol extractions were not significantly different, but triglycerides were barely recovered from serum by methanol extraction, requiring the use of isopropanol. However, once the serum was enriched with CD9 magnetic beads, triglycerides, and phospholipids were recovered at similar levels in both methanol and isopropanol extractions. Therefore, it is possible that the triglyceride fraction of the whole serum and the triglyceride fraction were enriched in CD9 magnetic beads differ in localization and properties. In addition, the variation per disease was small in general serum lipidomics; however, the difference per disease appeared larger when CD9 magnetic bead enrichment was employed.
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Affiliation(s)
- Suzumi M. Tokuoka
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8654, Japan; (S.M.T.); (Y.K.); (T.S.)
| | - Yoshihiro Kita
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8654, Japan; (S.M.T.); (Y.K.); (T.S.)
| | - Masaya Sato
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8654, Japan; (M.S.); (Y.Y.)
| | - Takao Shimizu
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8654, Japan; (S.M.T.); (Y.K.); (T.S.)
- National Center for Global Health and Medicine, Department of Lipid Signaling, Toyama 1-21-1, Shinjuku-ku, Tokyo 162-8655, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8654, Japan; (M.S.); (Y.Y.)
| | - Yoshiya Oda
- Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8654, Japan; (S.M.T.); (Y.K.); (T.S.)
- Correspondence: ; Tel.: +81-35-841-3540
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Multi-Omics Analysis to Generate Hypotheses for Mild Health Problems in Monkeys. Metabolites 2021; 11:metabo11100701. [PMID: 34677416 PMCID: PMC8538200 DOI: 10.3390/metabo11100701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 09/28/2021] [Accepted: 10/09/2021] [Indexed: 11/17/2022] Open
Abstract
Certain symptoms associated with mild sickness and lethargy have not been categorized as definitive diseases. Confirming such symptoms in captive monkeys (Macaca fascicularis, known as cynomolgus monkeys) can be difficult; however, it is possible to observe and analyze their feces. In this study, we investigated the relationship between stool state and various omics data by considering objective and quantitative values of stool water content as a phenotype for analysis. By examining the food intake of the monkeys and assessing their stool, urine, and plasma, we attempted to obtain a comprehensive understanding of the health status of individual monkeys and correlate it with the stool condition. Our metabolomics data strongly suggested that many lipid-related metabolites were correlated with the stool water content. The lipidomic analysis revealed the involvement of saturated and oxidized fatty acids, metallomics revealed the contribution of selenium (a bio-essential trace element), and intestinal microbiota analysis revealed the association of several bacterial species with the stool water content. Based on our results, we hypothesize that the redox imbalance causes minor health problems. However, it is not possible to make a definite conclusion using multi-omics alone, and other hypotheses could be proposed.
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Lu J, Lu Y, Ding Y, Xiao Q, Liu L, Cai Q, Kong Y, Bai Y, Yu T. DNLC: differential network local consistency analysis. BMC Bioinformatics 2019; 20:489. [PMID: 31874600 PMCID: PMC6929334 DOI: 10.1186/s12859-019-3046-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 08/21/2019] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. RESULTS In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. CONCLUSIONS The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.
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Affiliation(s)
- Jianwei Lu
- School of Software Engineering, Tongji University, Shanghai, China
- Institute of Advanced Translational Medicine, Tongji University, Shanghai, China
| | - Yao Lu
- School of Software Engineering, Tongji University, Shanghai, China
| | - Yusheng Ding
- School of Software Engineering, Tongji University, Shanghai, China
| | - Qingyang Xiao
- Department of Environmental Health, Emory University, Atlanta, GA USA
| | - Linqing Liu
- School of Software Engineering, Tongji University, Shanghai, China
| | - Qingpo Cai
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA
| | - Yunchuan Kong
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA
| | - Yun Bai
- Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Georgia Campus, Suwanee, GA USA
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA
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Ismail IT, Showalter MR, Fiehn O. Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics. Metabolites 2019; 9:metabo9100242. [PMID: 31640247 PMCID: PMC6835511 DOI: 10.3390/metabo9100242] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 10/11/2019] [Accepted: 10/15/2019] [Indexed: 12/30/2022] Open
Abstract
Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.
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Affiliation(s)
- Israa T Ismail
- National Liver Institute, Menoufia University, Shebeen El Kom 55955, Egypt.
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
| | - Megan R Showalter
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA.
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Stanstrup J, Broeckling CD, Helmus R, Hoffmann N, Mathé E, Naake T, Nicolotti L, Peters K, Rainer J, Salek RM, Schulze T, Schymanski EL, Stravs MA, Thévenot EA, Treutler H, Weber RJM, Willighagen E, Witting M, Neumann S. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019; 9:E200. [PMID: 31548506 PMCID: PMC6835268 DOI: 10.3390/metabo9100200] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 11/17/2022] Open
Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.
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Affiliation(s)
- Jan Stanstrup
- Preventive and Clinical Nutrition, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark.
| | - Corey D Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, CO 80523, USA.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 1098 XH Amsterdam, The Netherlands.
| | - Nils Hoffmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Thomas Naake
- Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
| | - Luca Nicolotti
- The Australian Wine Research Institute, Metabolomics Australia, PO Box 197, Adelaide SA 5064, Australia.
| | - Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy.
| | - Reza M Salek
- The International Agency for Research on Cancer, 150 cours Albert Thomas, CEDEX 08, 69372 Lyon, France.
| | - Tobias Schulze
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
| | - Michael A Stravs
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dubendorf, Switzerland.
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Sciences and Decision, MetaboHUB, Gif-Sur-Yvette F-91191, France.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
- Chair of Analytical Food Chemistry, Technische Universität München, 85354 Weihenstephan, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry (IPB Halle), Bioinformatics and Scientific Data, 06120 Halle, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz 5e, 04103 Leipzig, Germany.
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Jin Z, Kang J, Yu T. Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations. Bioinformatics 2019; 34:1555-1561. [PMID: 29272352 DOI: 10.1093/bioinformatics/btx816] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 12/19/2017] [Indexed: 12/20/2022] Open
Abstract
Motivation Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal. Results We proposed an imputation algorithm that incorporates the existing metabolic network, adduct ion relations even for unknown compounds, as well as linear and nonlinear associations between feature intensities to build a feature-level network. The algorithm uses support vector regression for missing value imputation based on features in the neighborhood on the network. We compared our proposed method with methods being widely used. As judged by the normalized root mean squared error in real data-based simulations, our proposed methods can achieve better accuracy. Availability and implementation The R package is available at http://web1.sph.emory.edu/users/tyu8/MINMA. Contact jiankang@umich.edu or tianwei.yu@emory.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhuxuan Jin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
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Misra BB. New tools and resources in metabolomics: 2016-2017. Electrophoresis 2018; 39:909-923. [PMID: 29292835 DOI: 10.1002/elps.201700441] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 12/17/2017] [Accepted: 12/18/2017] [Indexed: 01/07/2023]
Abstract
Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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